Artificial Intelligence Systems That Incorporate Expert Knowledge Related to Hypertension Treatments

1. One or more tangible, non-transitory, machine-readable media storing instructions that when executed by one or more processors effectuate operations comprising: obtaining, with an artificial intelligence (AI) application executed by a computer system, information about a current patient condition, wherein: the information relates to a patient, and the AI application comprises: a plurality of pharmaceutical-specific models each corresponding to a different class of pharmaceuticals, and an expert system to determine an updated prescription based on the patient condition and predicted patient responses from the pharmaceutical-specific models; inputting, with the computer system, at least some of the obtained information about the patient condition into the respective pharmaceutical-specific models and, in response: predicting respective changes in the condition of the patient responsive to respective changes in respective dosages of the respective classes of pharmaceuticals to obtain a set of predicted patient responses for the different classes of pharmaceuticals, selecting, based on the set of predicted patient responses, candidate classes of pharmaceuticals, determining differences between target dosages and the current dosages of the classes of pharmaceuticals, and based on the differences, determining respective priority scores of the respective classes of pharmaceuticals; inputting, with the computer system, into the expert system of the AI application, the candidate classes of pharmaceuticals and their respective priority scores and, in response to the inputting, determining an updated prescription; and storing, with the computer system, the updated prescription in memory.

2. The media of claim 1, wherein the pharmaceutical-specific models comprise at least two of the following: an angiotensin-converting enzyme (ACE) inhibitor model, an angiotensin receptor blocker model, a beta-blocker model, a mineralocorticoid-receptor antagonist (MRA) model, or a renin-angiotensin-system (RAS) blocker model.

3. The media of claim 1, wherein the pharmaceutical-specific models comprise each of the following: an angiotensin-converting enzyme (ACE) inhibitor model, an angiotensin receptor blocker model, a beta-blocker model, a mineralocorticoid-receptor antagonist (MRA) model, and a renin-angiotensin-system (RAS) blocker model.

4. The media of claim 1, wherein: at least some of the pharmaceutical-specific models comprise: a sub-model configured to determine a full-strength dosage of the respective class of pharmaceuticals for the patient based on the information about the current patient condition, an input configured to receive a current dosage of the respective class of pharmaceuticals for the patient from the information about the current patient condition, and a plurality of thresholds indicative of whether the current dosage is to be determined to be safe to increase, decrease, or leave unchanged for the patient; and the at least some of the pharmaceutical-specific models are configured to determine, based on the information about the current patient condition, a respective value indicative of whether the current dosage is determined to be safe and compare that value to the plurality of thresholds to determine whether to recommend a medical professional increase, decrease, or leave unchanged the current dosage.

5. The media of claim 4, wherein the AI application is configured to determine that more than a threshold number of candidate classes of pharmaceuticals are determined to be candidates to be increased and, in response, select a subset of the candidate classes of pharmaceuticals to be increased based on respective determined priority scores of the subset of the candidate classes of pharmaceuticals.

6. The media of claim 1, wherein: the AI application comprises a translator configured to translate a dosage of a given class of pharmaceuticals to a corresponding dosage of another class of pharmaceuticals; and a model specific to the another class of pharmaceuticals is configured to use the translated dosage to predict a patient response to changes in the another class of pharmaceuticals based on a current dosage in the given class of pharmaceuticals.

7. The media of claim 1, wherein the AI application is configured to: determine a value indicative of a difference in heart rate between a target heart rate of the patient and a patient heart rate indicated in the information about the current patient condition; and adjust or otherwise determine at least some of the priority scores based on the value indicative of the difference in heart rate.

8. The media of claim 1, wherein the AI application is configured to: determine a value indicative of a difference in thrombolytic predictive instrument (TPI) measurements between a target TPI measurement of the patient and a patient TPI measurement in the information about the current patient condition; and adjust or otherwise determine at least some priority scores based on the value indicative of the difference in TPI measurements.

9. The media of claim 1, wherein at least some of the pharmaceutical-specific models comprise: means for determining whether the respective class of pharmaceuticals is suitable for the patient; and means for determining a priority of the respective class of pharmaceuticals.

10. The media of claim 1, wherein the expert system comprises: a plurality of rules encoded as Boolean statements corresponding to nodes and edges of a rule-graph of the expert system; and a rules engine configured to: traverse the rule-graph by evaluating the Boolean statements based the candidate classes of pharmaceuticals and their respective determined priority scores, and output an updated prescription based on the evaluating.

11. The media of claim 1, wherein the expert system comprises a plurality of rules including at least two of the following: a rule responsive to whether fluid flow in the patient is negative; a rule responsive to whether fluid flow in the patient is neutral; a rule responsive to whether the patient is taking an antiarrhythmic agent; a rule responsive to whether to whether the patient has experienced atrial fibrillation; a rule responsive to an whether a threshold amount of dosages of different classes of pharmaceuticals are candidates to be changed concurrently; or a rule that adjusts the threshold amount of dosages of different classes of pharmaceuticals based on a blood sugar level of the patient in the information about the current patient condition.

12. The media of claim 1, wherein the expert system comprises a plurality of rules including each of the following: a rule responsive to whether fluid flow in the patient is negative; a rule responsive to whether fluid flow in the patient is neutral; a rule responsive to whether the patient is taking an antiarrhythmic agent; a rule responsive to whether to whether the patient has experienced atrial fibrillation; a rule responsive to an whether a threshold amount of dosages of different classes of pharmaceuticals are candidates to be changed concurrently; and a rule that adjusts the threshold amount of dosages of different classes of pharmaceuticals based on a blood sugar level of the patient in the information about the current patient condition.

13. The media of claim 1, wherein the expert system comprises a plurality of rules including each of the following: a rule that takes a variable; a rule that modifies a value of a variable in another rule conditional upon inputs; a rule that outputs a recommendation conditional upon inputs; and a rule that that outputs a prohibition conditional upon inputs.

14. The media of claim 1, wherein: the expert system comprises more than 250 rules; the prescription is updated more than 5 times for a given patient; the number of pharmaceutical-specific models is greater than or equal to 4; and the AI model is responsive to more than 10 different lab test measurements and demographic attributes.

15. The media of claim 1, wherein the expert system comprises rules encoding: steps for determining a response to a beta negative flow of the patient.

16. The media of claim 1, wherein the expert system comprises rules encoding: steps for determining a response to use of sotalol by the patient.

17. The media of claim 1, wherein the expert system comprises rules encoding: steps for determining a response to a fluid negative flow of the patient.

18. The media of claim 1, wherein the AI application is configured to present a recommended updated prescription in a user interface based on output of the expert system.

19. The media of claim 1, wherein the information about the patient comprises: impedance cardiograph data based upon variation in impedance measurements during heart beats from an alternating current applied to a set of four or more electrodes placed on the patient adjacent a neck of the patient and a diaphragm of the patient; age and gender of the patient; a value indicative of use of a loop diuretic with the patient; and values indicative of results of at least three of the following tests of the patient: a measurement of B-type natriuretic peptide (BNP) in blood of the patient, a measurement of N-terminal pro-BNP in blood of the patient, a metabolic panel indicative of an amount of electrolyte imbalance, kidney failure, or liver disease of the patient, a complete blood count (CBC) indicative of anemia, a thyroid tests indicative of an amount of thyroid hormone in blood of the patient, a measurement of Galectin-3 protein in blood of the patient, or a measurement of ST2 protein in blood of the patient.

20. A method, comprising: obtaining, with an artificial intelligence (AI) application executed by a computer system, information about a current patient condition, wherein: the information relates to a patient, and the AI application comprises: a plurality of pharmaceutical-specific models each corresponding to a different class of pharmaceuticals, and an expert system to determine an updated prescription based on the patient condition and predicted patient responses from the pharmaceutical-specific models; inputting, with the computer system, at least some of the obtained information about the patient condition into the respective pharmaceutical-specific models and, in response: predicting respective changes in the condition of the patient responsive to respective changes in respective dosages of the respective classes of pharmaceuticals to obtain a set of predicted patient responses for the different classes of pharmaceuticals, selecting, based on the set of predicted patient responses, candidate classes of pharmaceuticals, determining differences between target dosages and the current dosages of the classes of pharmaceuticals, and based on the differences, determining respective priority scores of the respective classes of pharmaceuticals; inputting, with the computer system, into the expert system of the AI application, the candidate classes of pharmaceuticals and their respective priority scores and, in response to the inputting, determining an updated prescription; and storing, with the computer system, the updated prescription in memory.